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 has been shown to be effective in producing approximate solutions to some vehicle routing problems (VRPs)%2C especially when using policies generated by encoder-decoder attention mechanisms. While these techniques have been quite successful for relatively simple problem instances%2C there are still under-researched and highly complex VRP variants for which no effective RL method has been demonstrated. In this work we focus on one such VRP variant%2C which contains multiple trucks and multi-leg routing requirements. In these problems%2C demand is required to move along sequences of nodes%2C instead of just from a start node to an end node. With the goal of making deep RL a viable strategy for real-world industrial-scale supply chain logistics%2C we develop new extensions to existing encoder-decoder attention models which allow them to handle multiple trucks and multi-leg routing requirements. Our models have the advantage that they can be trained for a small number of trucks and nodes%2C and then embedded into a large supply chain to yield solutions for larger numbers of trucks and nodes. We test our approach on a real supply chain environment arising in the operations of Japanese automotive parts manufacturer Aisin Corporation%2C and find that our algorithm outperforms Aisin's previous best solution.?quality=80&w=800)
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